Circular Economy is an economic concept that aims to maximise resource efficiency and minimise waste generation by recycling, reusing, remanufacturing, and repairing products to extend their lifespan and extract maximum value from them. Historically, the automotive industry has not focused on sustainable practices. In the last decade, automotive manufacturers have increasingly adopted circular economy practices driven by a combination of environmental concerns, resource scarcity, regulatory pressures, consumer demand, and the pursuit of new business opportunities. By embracing circularity, the industry aims to reduce its environmental impact, optimise resource use, and build a more sustainable and resilient future. The paper provides a review to present the concept of circular economy and to evaluate its current state in the automotive industry. The case study aims to provide a glimpse into the initiatives and strategies being made by the Audi Group to become circular. The research is descriptive and includes mainly qualitative methods using a content analysis of corporate sustainability reports, websites, and interviews to gather data.

The growing pace of the introduction of generative artificial intelligence (GenAI) into organizational processes is changing the way workers cooperate with technology. Based on Social Exchange Theory, we propose that the perception of employees regarding the value of GenAI-based agents, their vulnerability and privacy, and their self-concern would determine the collaboration with GenAI agents, which subsequently would predict the knowledge sharing, job thriving, and well-being. The findings show that perceived GenAI-based value has a strong positive impact on human-GenAI-based agent collaboration, but data vulnerability and privacy concerns have no significance. Interestingly, self-concern has a positive effect, which implies that identity-based fears can be used to drive active use of GenAI-based agents. Knowledge sharing, job thriving, and well-being are highly predicted by human-GenAI-based agent collaboration, and organizational exploratory innovation moderates these correlations. These results extrapolate the Social Exchange Theory to human-AI situations, dispel the assumptions of the consistently negative impact of risk perception, and emphasize the relevance of organizational climate to the implementation of the advantages of AI cooperation. The paper provides both theoretical and practical understanding of the way in which employees interact with GenAI-based agents to ensure that organizational learning and psychological outcomes of employees are achieved.

This research develops and assesses a model based on capabilities that explains how platform-based open innovation networks enhance sustainability through sequential mediation processes. By relying on concepts of reflexive innovation, ecosystem learning, and knowledge co-creation, the research shows that participation in platforms enhances the maturity of the circular learning ecosystems; therefore, promoting collaborative reflexive practice. Reflexivity leads to two outcomes: (a) an individual-level increase in circular literacy, which implies an increase in knowledge, skills, and behavioral orientation to circularity, and (b) system-level sustainable platform-based innovation outcomes, comprising the social and environmental impacts of the innovational processes. The outcomes endorse the proposed mediation directions. Interestingly, the digital inclusivity orientation was not associated with the reflexivity-literacy relationship, signifying that inclusion requires a deeper organizational integration in order to generate high capability impacts. The study enhances the literature on circular economy and innovation by introducing the concepts of reflexivity and circular literacy as the strategic abilities of a company and providing managers and policymakers with insights on how to build inclusive and sustainability-oriented platform ecosystems.

Purpose – The present study aims to examine how organisations adopt Generative AI and what are the key capabilities that influence the adoption of this technology. The study also investigates how Generative AI adoption influence market performance and sustainability performance. It additionally examines the moderating effect of regulatory support and organizational culture in influencing the association between Generative AI and firm performance. Design/methodology/approach – The study uses a mixed-methods design involving qualitative and quantitative data collection and analysis. The first stage begins with qualitative interviews followed by thematic analysis to establish leading capabilities behind Gen AI adoption, in the second stage, the data were collected from 385 respondents from different organizations which was then analysed using PLS-SEM structural equation modelling. Findings – The results observed that a firm’s digital transformation, innovation and marketing capabilities (MC) significantly enhance its Generative AI Adoption, which further influences firm performance. In addition, regulatory support emerges as a key moderator in driving DTC. Research limitations/implications – The findings emphasize that the firm should enhancing digital transformation capabilities, innovation capabilities and MC which can further strengthen the adoption of Generative AI and affect the firm market and sustainability performance. Whereas strengthening regulatory support can enhance the positive impact of DTC and Gen AI on firm sustainability performance. Originality/value – The study contributes to the literature on Generative AI by shaping an understanding of how the adoption of GenAI relates with the capability of firms in impacting sustainability performance. The research reports a critical gap in the literature by moving beyond the GenAI enthusiasm and assesses whether the advantages of adopting GenAI are durable as the technology diffuses across industries. The study contributes to literature by highlighting the role of regulatory support and determines that how environmental and firm-level factors jointly shape the effective and responsible capitalization of Generative AI for value creation over time.